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#Install packages

install.packages("BiocManager")
BiocManager::install("dada2", version = "3.11")

install.packages("qiime2R")
install.packages("tidyverse")
install.packages("biomformat")
install.packages('vegan')
install.packages('readr')
 
#Load packages

library("tidyverse")
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
-- Attaching packages --------------------------------------------------------------------------------------------- tidyverse 1.3.0 --
v ggplot2 3.3.2     v purrr   0.3.4
v tibble  3.0.3     v dplyr   1.0.2
v tidyr   1.1.2     v stringr 1.4.0
v readr   1.3.1     v forcats 0.5.0
-- Conflicts ------------------------------------------------------------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
library("dada2")
Loading required package: Rcpp
library("biomformat")
library('vegan')
Loading required package: permute
Loading required package: lattice
This is vegan 2.5-6
library('readr')
library("dplyr")     # To manipulate dataframes
library("readxl")    # To read Excel files into R
library("ggplot2")   # for high quality graphics
library("BiocManager")
Bioconductor version 3.11 (BiocManager 1.30.10), ?BiocManager::install for help
Bioconductor version '3.11' is out-of-date; the current release version '3.12' is available with R version '4.0'; see
  https://bioconductor.org/install
library("phyloseq")   
library("tidyr")
library("data.table")
data.table 1.13.0 using 2 threads (see ?getDTthreads).  Latest news: r-datatable.com

Attaching package: 㤼㸱data.table㤼㸲

The following objects are masked from 㤼㸱package:dplyr㤼㸲:

    between, first, last

The following object is masked from 㤼㸱package:purrr㤼㸲:

    transpose
#Merging tables for species abundance 1
#http://www.cookbook-r.com/Manipulating_data/Converting_data_between_wide_and_long_format/



###read in SILVA biom-taxonomy for SILVA ####################################################

taxonomy45 <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/V4V5_SILVA_taxonomy.csv")
taxonomy68 <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/taxonomy_SILVA_V6V8.csv")

head(taxonomy45)
#make a data.table
taxonomy45<-as.data.table(taxonomy45)
taxonomy68<-as.data.table(taxonomy68)
taxonomy45
taxonomy68

###read in otu table
otu45<- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/V4V5__ASV_table.csv", header = T) #,row.names=1
otu68<- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/V6V8__ASV_table.csv", header = T)
head(otu45) 
head(otu68)

#Convert to long format
otu_long45<-gather(otu45, Sample, Counts, "X122":"X83") #dashes got changed to dots for some reason
otu_long68<-gather(otu68, Sample, Counts, "X122.o":"X83.o") #dashes got changed to dots for some reason

#make a data. Table
otu_long45<-as.data.table(otu_long45)
otu_long68<-as.data.table(otu_long68)
otu_long45
otu_long68

###read in metadata file
metaD45<-read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/metadata_16S_V4V5_jonesSound2019.txt", sep = "")
metaD45<-as.data.table(metaD45)
metaD45$Sample<-as.character(metaD45$Sample)
metaD45

metaD68<-read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/metadata_16S_V6V8_jonesSound2019.txt", sep = "")
metaD68<-as.data.table(metaD68)
metaD68$Sample<-as.character(metaD68$Sample)
metaD68


# first set the keys to the common column:
setkey(otu_long68,Sample)
setkey(metaD68,Sample) 

setkey(otu_long45,Sample)
setkey(metaD45,Sample) 

# join the tables
total1_45 <- merge(otu_long45,metaD45,by="Sample")
total1_45

total1_68 <- merge(otu_long68,metaD68,by="Sample")
total1_68

#set key of total1_45 as the OTUID to join the taxonomy table
setkey(total1_45,OTUID)
setkey(taxonomy45,OTUID)

setkey(total1_68,OTUID)
setkey(taxonomy68,OTUID)

#merge to big table
total2_45 <- merge(total1_45,taxonomy45)
setkey(total2_45, Sample)
total2_45

total2_68 <- merge(total1_68,taxonomy68)
setkey(total2_68, Sample)
total2_68

#Final Joined table of SILVA
all_joined_SILVA45 <- metaD45[total2_45]
(all_joined_SILVA45)

all_joined_SILVA68 <- metaD68[total2_68]
(all_joined_SILVA68)




# Join table for Greengenes ####################################

#made above
otu_long45
otu_long68
metaD45
metaD68
total1_45
total1_68

taxonomyGG45 <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/taxonomy_greengenes_V4V5.csv")
taxonomyGG68 <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/taxonomy_greengenes_V6V8.csv")
taxonomyGG45 <- as.data.table(taxonomyGG45)
taxonomyGG68 <- as.data.table(taxonomyGG68)

setkey(taxonomyGG68,OTUID)
setkey(taxonomyGG45,OTUID)

total2_GG45 <- merge(total1_45,taxonomyGG45)
setkey(total2_GG45, Sample)
total2_GG45

total2_GG68 <- merge(total1_68,taxonomyGG68)
setkey(total2_GG68, Sample)
total2_GG68

#Final Joined table of GreenGenes
all_joined_GG45 <- metaD45[total2_GG45]
(all_joined_GG45)

all_joined_GG68 <- metaD68[total2_GG68]
(all_joined_GG68)





# Join table for RDP ####################################

#made above
otu_long45
otu_long68
metaD45
metaD68
total1_45
total1_68

taxonomyRDP45 <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/taxonomy_RDP_V4V5.csv")
taxonomyRDP68 <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/taxonomy_RDP_V6V8.csv")
taxonomyRDP45 <- as.data.table(taxonomyRDP45)
taxonomyRDP68 <- as.data.table(taxonomyRDP68)

setkey(taxonomyRDP68,OTUID)
setkey(taxonomyRDP45,OTUID)

total2_RDP45 <- merge(total1_45,taxonomyRDP45)
setkey(total2_RDP45, Sample)
total2_RDP45

total2_RDP68 <- merge(total1_68,taxonomyRDP68)
setkey(total2_RDP68, Sample)
total2_RDP68

#Final Joined table of GreenGenes
all_joined_RDP45 <- metaD45[total2_RDP45]
(all_joined_RDP45)

all_joined_RDP68 <- metaD68[total2_RDP68]
(all_joined_RDP68)
NA
NA
NA
NA

#####Proportion plot for each sample with both primer, based on phylum (?) ######
p45 <- ggplot(all_joined_SILVA45, aes(x = Counts, y = Sample, fill = Phylum)) + geom_bar(position = "fill", stat = "identity")
Error in ggplot(all_joined_SILVA45, aes(x = Counts, y = Sample, fill = Phylum)) : 
  could not find function "ggplot"
  
######TABLE 1: table of prevalence of bacterial counts according to each primer#####
#bacteria on x, count on y, two different graphs or two coloured bars on each graph

otu_tax_45 <- merge(otu_long45,taxonomy45,by="OTUID")
otu_tax_45

otu_tax_68 <- merge(otu_long68,taxonomy68,by="OTUID")
otu_tax_68

# Basic barplot
p<-ggplot(data = otu_tax_45, aes(y=Phylum, x=Counts/1000)) +
  geom_bar(stat="identity",position = position_dodge(width=1.5))
p



plot2 <- ggplot(NULL, aes(y=Phylum, x=Counts/1000)) + 
      geom_bar(data = otu_tax_45, stat = "identity", colour="red",width=0.4, position = position_dodge(width=0.5)) +
      geom_bar(data = otu_tax_68, stat = "identity", colour="blue",width=0.4, position = position_dodge(width=0.5))
plot2


all_joined_SILVA45

all_joined_SILVA68

### V4V5 ###
orderCount45<- select(all_joined_SILVA45, Order, Counts) #count by Order.
orderCount45<- orderCount45[order(orderCount45$Counts)]

chloroTable45 <- filter(orderCount45, Order == "Chloroplast") #Select only rows that are chloroplasts
chloroSum45 <- sum(chloroTable45$Counts)
chloroSum45 #this is the number of sequences associated with chloroplasts by V4V5
[1] 78158
### V6V8 ###
orderCount68<- select(all_joined_SILVA68, Order, Counts) #count by Order.
orderCount68<- orderCount68[order(orderCount68$Counts)]

chloroTable68 <- filter(orderCount68, Order == "Chloroplast") #Select only rows that are chloroplasts
chloroSum68 <- sum(chloroTable68$Counts)
chloroSum68 #this is the number of sequences associated with chloroplasts by V4V5
[1] 57941
#TODO
#Counts of archaea members SILVA

#Database like commands for tables!!!
#https://stackoverflow.com/questions/12353820/sort-rows-in-data-table-in-decreasing-order-on-string-key-order-x-v-gives-er

#For V4V5
all_joined_SILVA45
sortTable45 <- all_joined_SILVA45[order(rank(Kingdom),Phylum)] #sort by kingdom name
Archaeas45 <- filter(sortTable45, Kingdom == "Archaea") #Select only rows where Kingdom is Archaea.
Archaeas45

#Simple bar graph
archPerSample45 <-ggplot(Archaeas, aes(x=Counts, y=Sample)) +
  geom_bar(stat="identity") +
  theme_light()
archPerSample45


#For V6V8
all_joined_SILVA68
sortTable68 <- all_joined_SILVA68[order(rank(Kingdom),Phylum)] #sort by kingdom name
Archaeas68 <- filter(sortTable68, Kingdom == "Archaea") #Select only rows where Kingdom is Archaea.
Archaeas68

#Simple bar graph
archPerSample68 <-ggplot(Archaeas68, aes(x=Counts, y=Sample)) +
  geom_bar(stat="identity") +
  theme_light()
archPerSample68

orderNames_S68 <- c("Nitrosopumilales")
orderSums_S68 <- c() #sums of each of the orders

#loop to find the sums of each order and add to the vector
for (oName in orderNames_S68) {
  curOrderTable <- filter(SILVA_table68, Order == oName) #Select only rows with the current order name from the full table
  curOrderTable <- select(curOrderTable, Order, Counts) #makes a sub table of only the current table's order name and counts
  
  curOrderSum <- sum(curOrderTable$Counts) #sums all the counts of that order
  orderSums_S68 <- c(orderSums_S68, curOrderSum) 
}
(orderSums_S68)
[1] 8226
orderDataFrame_S68 <- data.frame(orderNames_S68, orderSums_S68)
orderDataFrame_S68

topArchGraph_S68 <-ggplot(orderDataFrame_S68, aes(x=orderSums_S68, y=orderNames_S68)) +
  geom_bar(stat="identity") +
  theme_light()
topArchGraph_S68

#TODO
#Phyloseq to get alpha diversity
library(phyloseq)

otu_mat<- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/V4V5__ASV_table.csv", header = T, row.names = 1) #,row.names=1
tax_mat <-  read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/V4V5_SILVA_taxonomy.csv", header = T, row.names = 1)
samples_df<-read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/metadata_16S_V4V5_jonesSound2019.txt", sep = "")

head(tax_mat)
head(otu_mat)

#assign row names
row.names(otu_mat) <- otu_mat$OTUID
#row.names(tax_mat) <- tax_mat$OTUID
#row.names(samples_df) <- samples_df$Site

#Transform into matrixes otu and tax tables (sample table can be left as data frame)
otu_mat <- as.matrix(otu_mat)
#tax_mat <- as.matrix(tax_mat)

class(tax_mat)

#Transform to phyloseq objects
class(otu_mat) <- "numeric" #NEED to convert to numeric in this way. Have to run whole chunk.

OTU = otu_table(otu_mat, taxa_are_rows = TRUE)
TAX = tax_table(tax_mat)
samples = sample_data(samples_df)

head(OTU)
head(TAX)

#taxa_names(OTU)
sample_names(OTU)
sample_names(TAX)

taxa_names(OTU)
taxa_names(TAX)

#Convert phyloseq
Perma <- phyloseq(OTU, TAX, samples)
Perma
#Making Bubble plots (TODO)

#V4V5 -----------------------------------------------------------------------------------------------------------------

###read in otu table of V4V5
otu45<- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/V4V5__ASV_table.csv") #,row.names=1
head(otu45) 
#Convert to long format
otu_long45<-gather(otu45, Sample, Counts, "X122":"X83") #dashes got changed to dots for some reason
#make a data. Table
otu_long45<-as.data.table(otu_long45)
otu_long45

###read in V4V5_SILVA_taxonomy.csv
taxonomy45 <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/V4V5_SILVA_taxonomy.csv")
head(taxonomy45)
#make a data.table
taxonomy45<-as.data.table(taxonomy45)
taxonomy45

###read in metadata file
metaD45<-read.csv2("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/metadata_16S_V4V5_jonesSound2019.txt")
metaD45<-as.data.table(metaD45)
head(metaD45)


write.csv(otu_long45, "G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/for_plot45.csv")
#remove 0s
for_plot45 <- filter(otu_long45, Counts!= 0)
#for relative abundance:
#change "Name" for the name of your column with group description
for_plot45$type<-as.factor(for_plot45$type)
#order
for_plot45$type<-factor(for_plot45$type, levels=c( "river", "natural biofilm", "artificial biofilm"))
#Now order your x axis (site names) <-- edit accordingly.
for_plot45$Site <- factor(for_plot45$Site, levels = c( "Sif_1","SI_2","SI_3", "S4_1", "Big_1", "BH_2", "BH_3", "Ram_1","RAM_3", "Bapt_1", "BP_2", "BP_3", "Nord_1", "N_2", "N_3", "Rose_1", "RMS_1", "RMS_2", "RM_3", "MM_1", "Mod_1", "MM_2", "MM_3", "TM_1", "T_2", "SM_2", "White_1", "WM_2", "WM_3", "GM_1", "Sturg_1", "ST_2", "Verm_1", "VM_1", "VA_1", "VM_2", "VM_3", "V3_1"))

#sweet- now plot (note that this is for raw counts; change for relative abundance)
theme_set(theme_bw())
col=c( "green","orange", "purple")## <- change accordingly, if desired-- see http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf
g <- ggplot(for_plot45, aes(x=Sample, y=Sample)) 
g + geom_point(aes(size=Counts)) +
  scale_colour_manual(values=col)+
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size=9))+
  theme(axis.text.y = element_text( size=9))+
  theme(axis.title.x=element_text(size=12))+
  theme(axis.title.y=element_text(size=12))
#export both bubble plots as PDF.


#V6V8 -----------------------------------------------------------------------------------------------------------------

###read in otu table of V6V8 
otu68<- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/V6V8__ASV_table.csv") #,row.names=1
head(otu68) 
#Convert to long format
otu_long68<-gather(otu68, Sample, Counts, "X122.o":"X83.o") #dashes got changed to dots for some reason
#make a data. Table
otu_long68<-as.data.table(otu_long68)
otu_long68

###read in biom-taxonomy
taxonomy68 <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/taxonomy_SILVA_V6V8.csv")
head(taxonomy68)
#make a data.table
taxonomy<-as.data.table(taxonomy68)
taxonomy68

###read in metadata file
metaD68<-read.csv2("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/metadata_16S_V6V8_jonesSound2019C.csv") 
metaD68<-as.data.table(metaD68)
head(metaD68)



write.csv(otu_long68,"G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/for_plot68.csv")
v68 <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/for_plot68.csv")
v68

#remove 0s
for_plot68 <- filter(otu_long68, Counts!= 0)
#for relative abundance:
#change "Name" for the name of your column with group description
for_plot68$type<-as.factor(for_plot68$type)
#order
for_plot68$type<-factor(for_plot68$type, levels=c( "river", "natural biofilm", "artificial biofilm"))
#Now order your x axis (site names) <-- edit accordingly.
for_plot68$Site <- factor(for_plot68$Site, levels = c( "Sif_1","SI_2","SI_3", "S4_1", "Big_1", "BH_2", "BH_3", "Ram_1","RAM_3", "Bapt_1", "BP_2", "BP_3", "Nord_1", "N_2", "N_3", "Rose_1", "RMS_1", "RMS_2", "RM_3", "MM_1", "Mod_1", "MM_2", "MM_3", "TM_1", "T_2", "SM_2", "White_1", "WM_2", "WM_3", "GM_1", "Sturg_1", "ST_2", "Verm_1", "VM_1", "VA_1", "VM_2", "VM_3", "V3_1"))

#sweet- now plot (note that this is for raw counts; change for relative abundance)
theme_set(theme_bw())
col=c( "green","orange", "purple")## <- change accordingly, if desired-- see http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf
g <- ggplot(for_plot68, aes(x=Sample, y=Sample))#colour=type
g + geom_point(aes(size=Counts)) +
  scale_colour_manual(values=col)+
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size=9))+
  theme(axis.text.y = element_text( size=9))+
  theme(axis.title.x=element_text(size=12))+
  theme(axis.title.y=element_text(size=12))
#export both bubble plots as PDF.

#Data processing with Pyloseq:https://vaulot.github.io/tutorials/Phyloseq_tutorial.html


otu_mat <-read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Example_Content/all_OTU_NMDSnu.csv", row.names=1) 
tax_mat <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Example_Content/biom-taxonomy.csv", row.names=1)
samples_df  <- as.data.frame(read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Example_Content/all_OTU_NMDS.csv"))

head(otu_mat)
head(samples_df)

#assign row names
row.names(otu_mat) <- otu_mat$OTUID
row.names(tax_mat) <- tax_mat$OTUID
row.names(samples_df) <- samples_df$Site

#Transform into matrixes otu and tax tables (sample table can be left as data frame)
otu_mat <- as.matrix(otu_mat)
tax_mat <- as.matrix(tax_mat)

class(otu_mat)

#Transform to phyloseq objects
class(otu_mat) <- "numeric" #NEED to convert to numeric in this way. Have to run whole chunk.

OTU = otu_table(otu_mat, taxa_are_rows = TRUE)
TAX = tax_table(a)
samples = sample_data(samples_df)

nrow(OTU)
nrow(TAX)
nrow(samples)

#Convert phyloseq
Perma <- phyloseq(OTU, TAX, samples)
Perma
---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Ctrl+Alt+I*.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Ctrl+Shift+K* to preview the HTML file).

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.


```{r}
#Install packages

install.packages("BiocManager")
BiocManager::install("dada2", version = "3.11")

install.packages("qiime2R")
install.packages("tidyverse")
install.packages("biomformat")
install.packages('vegan')
install.packages('readr')
 
```

```{r}
#Load packages

library("tidyverse")
library("dada2")
library("biomformat")
library('vegan')
library('readr')
library("dplyr")     # To manipulate dataframes
library("readxl")    # To read Excel files into R
library("ggplot2")   # for high quality graphics
library("BiocManager")
library("phyloseq")   
library("tidyr")
library("data.table")
```



```{r}
#Merging tables for species abundance 1
#http://www.cookbook-r.com/Manipulating_data/Converting_data_between_wide_and_long_format/



###read in SILVA biom-taxonomy for SILVA ####################################################

taxonomy45 <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/V4V5_SILVA_taxonomy.csv")
taxonomy68 <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/taxonomy_SILVA_V6V8.csv")

head(taxonomy45)
#make a data.table
taxonomy45<-as.data.table(taxonomy45)
taxonomy68<-as.data.table(taxonomy68)
taxonomy45
taxonomy68

###read in otu table
otu45<- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/V4V5__ASV_table.csv", header = T) #,row.names=1
otu68<- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/V6V8__ASV_table.csv", header = T)
head(otu45) 
head(otu68)

#Convert to long format
otu_long45<-gather(otu45, Sample, Counts, "X122":"X83") #dashes got changed to dots for some reason
otu_long68<-gather(otu68, Sample, Counts, "X122.o":"X83.o") #dashes got changed to dots for some reason

#make a data. Table
otu_long45<-as.data.table(otu_long45)
otu_long68<-as.data.table(otu_long68)
otu_long45
otu_long68

###read in metadata file
metaD45<-read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/metadata_16S_V4V5_jonesSound2019.txt", sep = "")
metaD45<-as.data.table(metaD45)
metaD45$Sample<-as.character(metaD45$Sample)
metaD45

metaD68<-read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/metadata_16S_V6V8_jonesSound2019.txt", sep = "")
metaD68<-as.data.table(metaD68)
metaD68$Sample<-as.character(metaD68$Sample)
metaD68


# first set the keys to the common column:
setkey(otu_long68,Sample)
setkey(metaD68,Sample) 

setkey(otu_long45,Sample)
setkey(metaD45,Sample) 

# join the tables
total1_45 <- merge(otu_long45,metaD45,by="Sample")
total1_45

total1_68 <- merge(otu_long68,metaD68,by="Sample")
total1_68

#set key of total1_45 as the OTUID to join the taxonomy table
setkey(total1_45,OTUID)
setkey(taxonomy45,OTUID)

setkey(total1_68,OTUID)
setkey(taxonomy68,OTUID)

#merge to big table
total2_45 <- merge(total1_45,taxonomy45)
setkey(total2_45, Sample)
total2_45

total2_68 <- merge(total1_68,taxonomy68)
setkey(total2_68, Sample)
total2_68

#Final Joined table of SILVA
all_joined_SILVA45 <- metaD45[total2_45]
(all_joined_SILVA45)

all_joined_SILVA68 <- metaD68[total2_68]
(all_joined_SILVA68)




# Join table for Greengenes ####################################

#made above
otu_long45
otu_long68
metaD45
metaD68
total1_45
total1_68

taxonomyGG45 <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/taxonomy_greengenes_V4V5.csv")
taxonomyGG68 <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/taxonomy_greengenes_V6V8.csv")
taxonomyGG45 <- as.data.table(taxonomyGG45)
taxonomyGG68 <- as.data.table(taxonomyGG68)

setkey(taxonomyGG68,OTUID)
setkey(taxonomyGG45,OTUID)

total2_GG45 <- merge(total1_45,taxonomyGG45)
setkey(total2_GG45, Sample)
total2_GG45

total2_GG68 <- merge(total1_68,taxonomyGG68)
setkey(total2_GG68, Sample)
total2_GG68

#Final Joined table of GreenGenes
all_joined_GG45 <- metaD45[total2_GG45]
(all_joined_GG45)

all_joined_GG68 <- metaD68[total2_GG68]
(all_joined_GG68)





# Join table for RDP ####################################

#made above
otu_long45
otu_long68
metaD45
metaD68
total1_45
total1_68

taxonomyRDP45 <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/taxonomy_RDP_V4V5.csv")
taxonomyRDP68 <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/taxonomy_RDP_V6V8.csv")
taxonomyRDP45 <- as.data.table(taxonomyRDP45)
taxonomyRDP68 <- as.data.table(taxonomyRDP68)

setkey(taxonomyRDP68,OTUID)
setkey(taxonomyRDP45,OTUID)

total2_RDP45 <- merge(total1_45,taxonomyRDP45)
setkey(total2_RDP45, Sample)
total2_RDP45

total2_RDP68 <- merge(total1_68,taxonomyRDP68)
setkey(total2_RDP68, Sample)
total2_RDP68

#Final Joined table of GreenGenes
all_joined_RDP45 <- metaD45[total2_RDP45]
(all_joined_RDP45)

all_joined_RDP68 <- metaD68[total2_RDP68]
(all_joined_RDP68)




```


```{r}

#####Proportion plot for each sample with both primer, based on phylum (?) ######
p45 <- ggplot(all_joined_SILVA45, aes(x = Counts, y = Sample, fill = Phylum)) + geom_bar(position = "fill", stat = "identity")
ggplot(otu_longN, aes(fill=Counts, y=1, x=1)) + 
    geom_bar(position="fill", stat="identity") + expand_limits(y = 9000000, x=-9999999)  # or some other arbitrarily large number
p45


p68 <- ggplot(all_joined_SILVA68, aes(x = Counts, y = Sample, fill = Phylum)) + geom_bar(position = "fill", stat = "identity")
ggplot(otu_longN, aes(fill=Counts, y=1, x=1)) + 
    geom_bar(position="fill", stat="identity") + expand_limits(y = 9000000, x=-9999999)  # or some other arbitrarily large number
p68
  
```



```{r}
  
######TABLE 1: table of prevalence of bacterial counts according to each primer#####
#bacteria on x, count on y, two different graphs or two coloured bars on each graph

otu_tax_45 <- merge(otu_long45,taxonomy45,by="OTUID")
otu_tax_45

otu_tax_68 <- merge(otu_long68,taxonomy68,by="OTUID")
otu_tax_68

# Basic barplot
p<-ggplot(data = otu_tax_45, aes(y=Phylum, x=Counts/1000)) +
  geom_bar(stat="identity",position = position_dodge(width=1.5))
p


plot2 <- ggplot(NULL, aes(y=Phylum, x=Counts/1000)) + 
      geom_bar(data = otu_tax_45, stat = "identity", colour="red",width=0.4, position = position_dodge(width=0.5)) +
      geom_bar(data = otu_tax_68, stat = "identity", colour="blue",width=0.4, position = position_dodge(width=0.5))
plot2
```


```{r}
#Find top 20 most abundant orders:

taxonomy45
taxonomy68
all_joined_SILVA45
all_joined_SILVA68

#Sort out the counts of Orders
orderCount45<- as.data.table(table(all_joined_SILVA45$Order)) #count by Order.
orderCount45<- orderCount45[order(orderCount45$N)]
orderCount45 #V1 is the Order name, N is the count.

orderCount68<- as.data.table(table(all_joined_SILVA68$Order)) #count by Order.
orderCount68<- orderCount68[order(orderCount68$N)]
orderCount68 #V1 is the Order name, N is the count.

#Get top Orders
topTaxa45 <- slice_tail(orderCount45, n=20) #get the bottom 20 counts (it's by ascending order)
topTaxa45

topTaxa68 <- slice_tail(orderCount68,n=20)
topTaxa68

p<-ggplot(topTaxa45, aes(x=N, y=V1)) +
  geom_bar(stat="identity") +
  theme_light()
p

p<-ggplot(topTaxa68, aes(x=N, y=V1)) +
  geom_bar(stat="identity") +
  theme_light()
p

```


```{r}
#TABLE 3: no of sequences associated with chloroplasts per primer

all_joined_SILVA45
all_joined_SILVA68

### V4V5 ###
orderCount45<- select(all_joined_SILVA45, Order, Counts) #count by Order.
orderCount45<- orderCount45[order(orderCount45$Counts)]

chloroTable45 <- filter(orderCount45, Order == "Chloroplast") #Select only rows that are chloroplasts
chloroSum45 <- sum(chloroTable45$Counts)
chloroSum45 #this is the number of sequences associated with chloroplasts by V4V5

### V6V8 ###
orderCount68<- select(all_joined_SILVA68, Order, Counts) #count by Order.
orderCount68<- orderCount68[order(orderCount68$Counts)]

chloroTable68 <- filter(orderCount68, Order == "Chloroplast") #Select only rows that are chloroplasts
chloroSum68 <- sum(chloroTable68$Counts)
chloroSum68 #this is the number of sequences associated with chloroplasts by V4V5



```
```{r}
#FIGURE 7: taxonomic panel delineating top 20 (if there are top 20) chloroplast orders per primer and database

### V4V5 ###
orderCount45<- select(all_joined_SILVA45, Order, Class, Counts) #count and Order.
orderCount45<- orderCount45[order(orderCount45$Counts)] #sort by count
chloroTable45 <- filter(orderCount45, Order == "Chloroplast") #Select only rows that are chloroplasts
tail(chloroTable45,20) #top 20 counts.

### V6V8 ###
orderCount68<- select(all_joined_SILVA68, Order, Counts) #count by Order.
orderCount68<- orderCount68[order(orderCount68$Counts)]
chloroTable68 <- filter(orderCount68, Order == "Chloroplast") #Select only rows that are chloroplasts
tail(chloroTable68,20) #top 20 counts.

#What are the order names?? They're all just called "chloroplast".

```




```{r}
#TODO
#Counts of archaea members SILVA

#Database like commands for tables!!!
#https://stackoverflow.com/questions/12353820/sort-rows-in-data-table-in-decreasing-order-on-string-key-order-x-v-gives-er

#For V4V5
all_joined_SILVA45
sortTable45 <- all_joined_SILVA45[order(rank(Kingdom),Phylum)] #sort by kingdom name
Archaeas45 <- filter(sortTable45, Kingdom == "Archaea") #Select only rows where Kingdom is Archaea.
Archaeas45

#Simple bar graph
archPerSample45 <-ggplot(Archaeas, aes(x=Counts, y=Sample)) +
  geom_bar(stat="identity") +
  theme_light()
archPerSample45


#For V6V8
all_joined_SILVA68
sortTable68 <- all_joined_SILVA68[order(rank(Kingdom),Phylum)] #sort by kingdom name
Archaeas68 <- filter(sortTable68, Kingdom == "Archaea") #Select only rows where Kingdom is Archaea.
Archaeas68

#Simple bar graph
archPerSample68 <-ggplot(Archaeas68, aes(x=Counts, y=Sample)) +
  geom_bar(stat="identity") +
  theme_light()
archPerSample68
```

```{r}
# taxonomic barplot panel demonstrating top 20 archaeal orders for each database

#V4V5 tables
SILVA_table45 <- all_joined_SILVA45[order(rank(Kingdom),Phylum)] #sort by kingdom name
arcOnly_S45 <- filter(SILVA_table45, Kingdom == "Archaea") #Select only rows where Kingdom is Archaea.
RDP_table45 <- all_joined_RDP45[order(rank(Kingdom),Phylum)] #sort by kingdom name
arcOnly_R45 <- filter(RDP_table45, Kingdom == "Archaea") #Select only rows where Kingdom is Archaea.
RDP_table45
arcOnly_S45

#V6V8 tables
SILVA_table68 <- all_joined_SILVA68[order(rank(Kingdom),Phylum)] #sort by kingdom name
arcOnly_S68 <- filter(SILVA_table68, Kingdom == "Archaea") #Select only rows where Kingdom is Archaea.
RDP_table68 <- all_joined_RDP68[order(rank(Kingdom),Phylum)] #sort by kingdom name
arcOnly_R68 <- filter(RDP_table68, Kingdom == "Bacteria") #Select only rows where Kingdom is Archaea.

'''
#only look at orders of Archaea
arcOnly45 <- filter(SILVA_table45, Kingdom == "Archaea") #Select only rows where Kingdom is Archaea.
arcSort45 <- arcOnly45[order(rank(Counts),Order)] #sort by kingdom name
arcSort45
arcTop20_45 <- slice_tail(arcSort45, n=20) #get the bottom 20 counts (its by ascending order)
arcTop20_45

#V6V8
arcOnly68 <- filter(SILVA_table68, Kingdom == "Archaea") #Select only rows where Kingdom is Archaea.
arcSort68 <- arcOnly68[order(rank(Counts),Order)] #sort by kingdom name
arcSort68
arcTop20_68 <- slice_tail(arcSort68, n=20) #get the bottom 20 counts (its by ascending order)
arcTop20_68
'''
#dealing with dupes and distinct values:
#https://www.datanovia.com/en/lessons/identify-and-remove-duplicate-data-in-r/
#summing
#https://stackoverflow.com/questions/9676212/how-to-sum-data-frame-column-values


#V4V5 ###################

#SILVA ###
archSubset_S45 <- select(arcOnly45, Order) #Only get the names of the orders
archDistinct_S45 <- distinct(archSubset_S45) #Removes duplicates. Now it's a list of distinct orders.
archDistinct_S45

orderNames_S45 <- c("Lokiarchaeia","Bathyarchaeia","Thermoplasmata","Halobacteria","Woesearchaeia","Nanohaloarchaeia","Nitrososphaeria","Marine Benthic Group A")
orderSums_S45 <- c() #sums of each of the orders

#loop to find the sums of each order and add to the vector
for (oName in orderNames_S45) {
  curOrderTable <- filter(SILVA_table45, Order == oName) #Select only rows with the current order name from the full table
  curOrderTable <- select(curOrderTable, Order, Counts) #makes a sub table of only the current table's order name and counts
  
  curOrderSum <- sum(curOrderTable$Counts) #sums all the counts of that order
  orderSums_S45 <- c(orderSums_S45, curOrderSum) 
}
(orderSums_S45)

orderDataFrame_S45 <- data.frame(orderNames_S45, orderSums_S45)
orderDataFrame_S45

topArchGraph_S45 <-ggplot(orderDataFrame_S45, aes(x=orderSums_S45, y=orderNames_S45)) +
  geom_bar(stat="identity") +
  theme_light()
topArchGraph_S45


#RDP ### THERE ARE NO ARCHAEA??
archSubset_R45 <- select(arcOnly_R45, Order) #Only get the names of the orders
archDistinct_R45 <- distinct(archSubset_R45) #Removes duplicates. Now it's a list of distinct orders.
archDistinct_R45

orderNames_R45 <-c() #There are no archaea from the RDP database...?
orderSums_R45 <- c() #sums of each of the orders

#loop to find the sums of each order and add to the vector
for (oName in orderNames_R45) {
  curOrderTable <- filter(RDP_table45, Order == oName) #Select only rows with the current order name from the full table
  curOrderTable <- select(curOrderTable, Order, Counts) #makes a sub table of only the current table's order name and counts
  
  curOrderSum <- sum(curOrderTable$Counts) #sums all the counts of that order
  orderSums_R45 <- c(orderSums_S45, curOrderSum) 
}
(orderSums_R45)

orderDataFrame_R45 <- data.frame(orderNames_R45, orderSums_R45)
orderDataFrame_R45

topArchGraph_S45 <-ggplot(orderDataFrame_S45, aes(x=orderSums_S45, y=orderNames_S45)) +
  geom_bar(stat="identity") +
  theme_light()
topArchGraph_S45









#V6V8 ###################

#SILVA ###
archSubset <- select(arcOnly68, Order) #Only get the names of the orders
archDistinct <- distinct(archSubset) #Removes duplicates. Now it's a list of distinct orders.
archDistinct

orderNames_S68 <- c("Nitrosopumilales")
orderSums_S68 <- c() #sums of each of the orders

#loop to find the sums of each order and add to the vector
for (oName in orderNames_S68) {
  curOrderTable <- filter(SILVA_table68, Order == oName) #Select only rows with the current order name from the full table
  curOrderTable <- select(curOrderTable, Order, Counts) #makes a sub table of only the current table's order name and counts
  
  curOrderSum <- sum(curOrderTable$Counts) #sums all the counts of that order
  orderSums_S68 <- c(orderSums_S68, curOrderSum) 
}
(orderSums_S68)

orderDataFrame_S68 <- data.frame(orderNames_S68, orderSums_S68)
orderDataFrame_S68

topArchGraph_S68 <-ggplot(orderDataFrame_S68, aes(x=orderSums_S68, y=orderNames_S68)) +
  geom_bar(stat="identity") +
  theme_light()
topArchGraph_S68


#There is no archaea is RDP of V6V8

```

```{r}
#TODO
#Phyloseq to get alpha diversity
library(phyloseq)

otu_mat<- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/V4V5__ASV_table.csv", header = T, row.names = 1) #,row.names=1
tax_mat <-  read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/V4V5_SILVA_taxonomy.csv", header = T, row.names = 1)
samples_df<-read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/metadata_16S_V4V5_jonesSound2019.txt", sep = "")

head(tax_mat)
head(otu_mat)

#assign row names
row.names(otu_mat) <- otu_mat$OTUID
#row.names(tax_mat) <- tax_mat$OTUID
#row.names(samples_df) <- samples_df$Site

#Transform into matrixes otu and tax tables (sample table can be left as data frame)
otu_mat <- as.matrix(otu_mat)
#tax_mat <- as.matrix(tax_mat)

class(tax_mat)

#Transform to phyloseq objects
class(otu_mat) <- "numeric" #NEED to convert to numeric in this way. Have to run whole chunk.

OTU = otu_table(otu_mat, taxa_are_rows = TRUE)
TAX = tax_table(tax_mat)
samples = sample_data(samples_df)

head(OTU)
head(TAX)

#taxa_names(OTU)
sample_names(OTU)
sample_names(TAX)

taxa_names(OTU)
taxa_names(TAX)

#Convert phyloseq
Perma <- phyloseq(OTU, TAX, samples)
Perma






```

```{r}
#Making Bubble plots (TODO)

#V4V5 -----------------------------------------------------------------------------------------------------------------

###read in otu table of V4V5
otu45<- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/V4V5__ASV_table.csv") #,row.names=1
head(otu45) 
#Convert to long format
otu_long45<-gather(otu45, Sample, Counts, "X122":"X83") #dashes got changed to dots for some reason
#make a data. Table
otu_long45<-as.data.table(otu_long45)
otu_long45

###read in V4V5_SILVA_taxonomy.csv
taxonomy45 <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/V4V5_SILVA_taxonomy.csv")
head(taxonomy45)
#make a data.table
taxonomy45<-as.data.table(taxonomy45)
taxonomy45

###read in metadata file
metaD45<-read.csv2("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/metadata_16S_V4V5_jonesSound2019.txt")
metaD45<-as.data.table(metaD45)
head(metaD45)


write.csv(otu_long45, "G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V4V5/for_plot45.csv")
#remove 0s
for_plot45 <- filter(otu_long45, Counts!= 0)
#for relative abundance:
#change "Name" for the name of your column with group description
for_plot45$type<-as.factor(for_plot45$type)
#order
for_plot45$type<-factor(for_plot45$type, levels=c( "river", "natural biofilm", "artificial biofilm"))
#Now order your x axis (site names) <-- edit accordingly.
for_plot45$Site <- factor(for_plot45$Site, levels = c( "Sif_1","SI_2","SI_3", "S4_1", "Big_1", "BH_2", "BH_3", "Ram_1","RAM_3", "Bapt_1", "BP_2", "BP_3", "Nord_1", "N_2", "N_3", "Rose_1", "RMS_1", "RMS_2", "RM_3", "MM_1", "Mod_1", "MM_2", "MM_3", "TM_1", "T_2", "SM_2", "White_1", "WM_2", "WM_3", "GM_1", "Sturg_1", "ST_2", "Verm_1", "VM_1", "VA_1", "VM_2", "VM_3", "V3_1"))

#sweet- now plot (note that this is for raw counts; change for relative abundance)
theme_set(theme_bw())
col=c( "green","orange", "purple")## <- change accordingly, if desired-- see http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf
g <- ggplot(for_plot45, aes(x=Sample, y=Sample)) 
g + geom_point(aes(size=Counts)) +
  scale_colour_manual(values=col)+
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size=9))+
  theme(axis.text.y = element_text( size=9))+
  theme(axis.title.x=element_text(size=12))+
  theme(axis.title.y=element_text(size=12))
#export both bubble plots as PDF.


#V6V8 -----------------------------------------------------------------------------------------------------------------

###read in otu table of V6V8 
otu68<- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/V6V8__ASV_table.csv") #,row.names=1
head(otu68) 
#Convert to long format
otu_long68<-gather(otu68, Sample, Counts, "X122.o":"X83.o") #dashes got changed to dots for some reason
#make a data. Table
otu_long68<-as.data.table(otu_long68)
otu_long68

###read in biom-taxonomy
taxonomy68 <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/taxonomy_SILVA_V6V8.csv")
head(taxonomy68)
#make a data.table
taxonomy<-as.data.table(taxonomy68)
taxonomy68

###read in metadata file
metaD68<-read.csv2("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/metadata_16S_V6V8_jonesSound2019C.csv") 
metaD68<-as.data.table(metaD68)
head(metaD68)



write.csv(otu_long68,"G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/for_plot68.csv")
v68 <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/V6V8/for_plot68.csv")
v68

#remove 0s
for_plot68 <- filter(otu_long68, Counts!= 0)
#for relative abundance:
#change "Name" for the name of your column with group description
for_plot68$type<-as.factor(for_plot68$type)
#order
for_plot68$type<-factor(for_plot68$type, levels=c( "river", "natural biofilm", "artificial biofilm"))
#Now order your x axis (site names) <-- edit accordingly.
for_plot68$Site <- factor(for_plot68$Site, levels = c( "Sif_1","SI_2","SI_3", "S4_1", "Big_1", "BH_2", "BH_3", "Ram_1","RAM_3", "Bapt_1", "BP_2", "BP_3", "Nord_1", "N_2", "N_3", "Rose_1", "RMS_1", "RMS_2", "RM_3", "MM_1", "Mod_1", "MM_2", "MM_3", "TM_1", "T_2", "SM_2", "White_1", "WM_2", "WM_3", "GM_1", "Sturg_1", "ST_2", "Verm_1", "VM_1", "VA_1", "VM_2", "VM_3", "V3_1"))

#sweet- now plot (note that this is for raw counts; change for relative abundance)
theme_set(theme_bw())
col=c( "green","orange", "purple")## <- change accordingly, if desired-- see http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf
g <- ggplot(for_plot68, aes(x=Sample, y=Sample))#colour=type
g + geom_point(aes(size=Counts)) +
  scale_colour_manual(values=col)+
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size=9))+
  theme(axis.text.y = element_text( size=9))+
  theme(axis.title.x=element_text(size=12))+
  theme(axis.title.y=element_text(size=12))
#export both bubble plots as PDF.
```

```{r}
#NMDS
#example with randomized table from https://jonlefcheck.net/2012/10/24/nmds-tutorial-in-r/

#on my data:
otu_og3<- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Real_Content/Data_Files/both_ASV_tables3.csv", row.names = 1) #Now with the real data

otu_og3
ncol(otu_og3)
head(otu_og3)

otu <- t(otu_og3) #transpose
class(otu)
nrow(otu)
#head(otu)

rownames(otu)

#set grouping info
grouping_info <- data.frame(row.names=(rownames(otu)),t(as.data.frame(strsplit(rownames(otu),"_"))))
class(grouping_info)
grouping_info

#class(otu)<-("numeric") #needed when row names weren't removed
otu_trim <- na.omit(otu) #trim extra missing data as per https://stat.ethz.ch/pipermail/r-help//2013-May/353210.html
nrow(otu_trim)

my_NMDS<-metaMDS(otu,distance = "bray", k = 2, trymax = 50)

#View NMDS straight, check correctness
my_NMDS
stressplot(my_NMDS)
plot(my_NMDS)
nrow(my_NMDS$points)

#extract NMDS scores (x and y coordinates)
data.scores = as.data.frame(scores(my_NMDS))
colnames(data.scores)

ncol(grouping_info)

#metaMDS documentation full: https://www.rdocumentation.org/packages/vegan/versions/1.15-1/topics/metaMDS
#framing the NMDS data
NMDS=data.frame(x=my_NMDS$point[,1],y=my_NMDS$point[,2]) #Only uses point data (rest is not relevant in this case).


#Lists are columns of the metadata table for V4V5. Used for making the NMDS table

SampleNameV4V5<-c("3_S51","5_S21","6_S53","8_S54","14_S124","16_S90","20_S47","23_S88","25_S38","29_S85","31_S91","32_S89","33_S86","37_S78","39_S33","43_S34","46_S79","48_S80","56_S77","57_S67","65_S69","75_S25","81_S70","83_S28","122_S127","155_S104","157_S119")
SampleNameV6V8<-c("c3-o_S16","5-o_S16","6-o_S19","8-o_S4","14-o_S63","16-o_S18","20-o_S64","23-o_S65","25-o_S57","29-o_S7","31-o_S20","32-o_S5","33-o_S6","37-o_S61","39-o_S11","43-o_S10","46-o_S60","48-o_S59","56-o_S8","57-o_S62","65-o_S14","75-o_S58","81-o_S66","83-o_S13","122-o_S17","155-o_S3","157-o_S15")
SampleName <- c(SampleNameV4V5,SampleNameV6V8) #combine the two vectors from V4V5 and V6V8


TransectV4V5<-c("Sverdrup_distal","Fram_Fiord","Sverdrup_distal","Sverdrup_distal","Fram_Fiord","Sverdrup_distal","Sverdrup_glacier","Sverdrup_glacier","Sverdrup_glacier_repeat","Sverdrup_glacier","Sverdrup_distal","Sverdrup_glacier","Sverdrup_glacier","Sverdrup_glacier","Belcher_4","Belcher_4","Sverdrup_glacier","Sverdrup_glacier","Belcher_4","Jakeman_4","Jakeman_5","Fram_Fiord","Jakeman_5","Belcher_1","Subglacial","Sydkap_3","Grise_Fiord_2")
TransectV6V8<-c("Sverdrup_distal","Fram_Fiord","Sverdrup_distal","Sverdrup_distal","Fram_Fiord","Sverdrup_distal","Sverdrup_glacier","Sverdrup_glacier","Sverdrup_glacier_repeat","Sverdrup_glacier","Sverdrup_distal","Sverdrup_glacier","Sverdrup_glacier","Sverdrup_glacier","Belcher_4","Belcher_4","Sverdrup_glacier","Sverdrup_glacier","Belcher_4","Jakeman_4","Jakeman_5","Fram_Fiord","Jakeman_5","Belcher_1","Subglacial","Sydkap_3","Grise_Fiord_2")
Transect <- c(TransectV4V5,TransectV6V8)

DepthV4V5<-c("15", "5", "50", "200", "10", "125","47", "40", "17","4", "350", "67","12", "7", "5", "20", "20", "115", "140", "5", "5", "0", "27", "95", "112", "7", "145")
DepthV6V8<-c("15", "5", "50", "200", "10", "125","47", "40", "17","4", "350", "67","12", "7", "5", "20", "20", "115", "140", "5", "5", "0", "27", "95", "112", "7", "145")
Depth <- c(DepthV4V5,DepthV6V8)

StationV4V5<- c("VIO_33","VIO_2","VIO_33","VIO_33","VIO_2","VIO_32","VIO_30","VIO_31","VIO_24","VIO_28","VIO_32","VIO_31","VIO_28","VIO_22","VIO_13","VIO_13","VIO_22","VIO_22","VIO_13","VIO_6","VIO_7","VIO_9","VIO_7","VIO_10","Glacial_sample","VIO_39","VIO_44")
StationV6V8<- c("VIO_33","VIO_2","VIO_33","VIO_33","VIO_2","VIO_32","VIO_30","VIO_31","VIO_24","VIO_28","VIO_32","VIO_31","VIO_28","VIO_22","VIO_13","VIO_13","VIO_22","VIO_22","VIO_13","VIO_6","VIO_7","VIO_9","VIO_7","VIO_10","Glacial_sample","VIO_39","VIO_44")
Station <- c(StationV4V5,StationV6V8)

IDV4V5<-c("3","5","6","8","14","16","20","23","25","29","31","32","33","37","39","43","46","48","56","57","65","75","81","83","122","155","157")
IDV6V8<-c("3-o","5-o","6-o","8-o","14-o","16-o","20-o","23-o","25-o","29-o","31-o","32-o","33-o","37-o","39-o","43-o","46-o","48-o","56-o","57-o","65-o","75-o","
81-o","83-o","122-o","155-o","157-o")
ID <- c(IDV4V5,IDV6V8)

Primer <- c("V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V4V5","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8","V6V8")


#add this to your NMDS data frame as a new column
NMDS$ID<-ID
NMDS$SampleName<-SampleName
NMDS$Transect<-Transect
NMDS$Depth<-Depth
NMDS$Station<-Station
NMDS$Primer<-Primer

#order your Group column according what you would like, here for example, according to  ID number:
NMDS$ID<-factor(NMDS$ID, levels=ID)

NMDS #This is a dataframe that has the metadata manually merged into it, as well as the X and Y values from the NMDS analysis. This is what is used to create the plot.

#Graphing NMDS
#Graph with all trimmings
library(ggplot2)

xx = ggplot(NMDS, aes(x = x, y = y)) + 
    geom_point(aes(size = as.numeric(Depth), shape = Primer, colour = Transect))+
  
    theme(axis.text.y = element_text(colour = "black", size = 12, face = "bold"), 
    axis.text.x = element_text(colour = "black", face = "bold", size = 12), 
    legend.text = element_text(size = 12, face ="bold", colour ="black"), 
    legend.position = "right", axis.title.y = element_text(face = "bold", size = 14), 
    axis.title.x = element_text(face = "bold", size = 14, colour = "black"), 
    legend.title = element_text(size = 14, colour = "black", face = "bold"), 
    panel.background = element_blank(), panel.border = element_rect(colour = "black", fill = NA, size = 1.2),
    legend.key=element_blank()) + 
    labs(x = "x", colour = "Transect", y = "y", shape = "Primer")  
 
xx


primer_only = ggplot(NMDS, aes(x = x, y = y)) + 
    ggtitle("NMDS of samples compared by primer")+
    geom_point(aes(colour = Primer), size = 3)+
    geom_point(shape = 1,size = 3,colour = "black")+
    geom_text(aes(label=ID),hjust=1.25, vjust=1.25,size=2.5)+

  
    theme(axis.text.y = element_text(colour = "black", size = 12, face = "bold"), 
    axis.text.x = element_text(colour = "black", face = "bold", size = 12), 
    legend.text = element_text(size = 12, face ="bold", colour ="black"), 
    legend.position = "right", axis.title.y = element_text(face = "bold", size = 14), 
    axis.title.x = element_text(face = "bold", size = 14, colour = "black"), 
    legend.title = element_text(size = 14, colour = "black", face = "bold"), 
    panel.background = element_blank(), panel.border = element_rect(colour = "black", fill = NA, size = 1.2),
    legend.key=element_blank()) + 
    labs(x = "x", y = "y", colour = "Primer")  
 
primer_only


```

```{r}
#Data processing with Pyloseq:https://vaulot.github.io/tutorials/Phyloseq_tutorial.html


otu_mat <-read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Example_Content/all_OTU_NMDSnu.csv", row.names=1) 
tax_mat <- read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Example_Content/biom-taxonomy.csv", row.names=1)
samples_df  <- as.data.frame(read.csv("G:/Desktop/Folders/Edu/2] EAS/Thesis/Example_Content/all_OTU_NMDS.csv"))

head(otu_mat)
head(samples_df)

#assign row names
row.names(otu_mat) <- otu_mat$OTUID
row.names(tax_mat) <- tax_mat$OTUID
row.names(samples_df) <- samples_df$Site

#Transform into matrixes otu and tax tables (sample table can be left as data frame)
otu_mat <- as.matrix(otu_mat)
tax_mat <- as.matrix(tax_mat)

class(otu_mat)

#Transform to phyloseq objects
class(otu_mat) <- "numeric" #NEED to convert to numeric in this way. Have to run whole chunk.

OTU = otu_table(otu_mat, taxa_are_rows = TRUE)
TAX = tax_table(a)
samples = sample_data(samples_df)

nrow(OTU)
nrow(TAX)
nrow(samples)

#Convert phyloseq
Perma <- phyloseq(OTU, TAX, samples)
Perma



```


























